Learning from Irregular Time Series.
Standard machine learning techniques often fail for ITS as they assume fixed-length and regularly-spaced inputs [1]}, which are especially rare in important medical settings [2]}.
To bridge this gap, myriad recent works learn from ITS directly, developing models that take irregular series as inputs.
Some approaches augment RNNs by either including auxiliary information such as a missingness-indicator [3]} or time-since-last-observation [4]} as extra features to preserve properties found in the irregularity.
Others build more complex value estimators by either learning generative models [5]}, using gaussian kernel adapters [6]}, [7]}, set functions [8]}, or including decay mechanisms in Recurrent Neural Networks (RNN) to encode information-loss when variables go unobserved over long periods of time [9]}, [4]}.
Some recent works have begun parameterizing ordinary differential equations to serve as time series models [11]}, [12]}, [13]}, [14]},
Some very recent models have also begun to integrate attention mechanisms into this estimation process [15]}, [16]}, [17]}.
